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Abstract:

A method for detecting a person using an image capture device obtains a
plurality of images of a monitored scene captured by a lens module of the
image capture device, and detects an area of motion in the monitored
scene from the obtained images. The method further checks for a person in
the area of motion, and adjusts the lens module of the image capture
device according to movement data of the area of motion to focus the lens
module on the person.

Claims:

1. A method for detecting a person using an image capture device, the
method comprising: obtaining a plurality of images of a monitored scene,
the images being captured using a lens module of the image capture
device; detecting an area of motion in the monitored scene from the
obtained images; and checking for a person in the area of motion using a
person detection method.

2. The method according to claim 1, wherein the step of detecting an area
of motion in the monitored scene from the obtained images comprises:
obtaining a first image of the monitored scene at a first time from the
obtained images, and calculating characteristic values of the first
image; obtaining a second image of the monitored scene at a second time
continuous with the first time, and calculating the characteristic values
of the second image; comparing the first image with the second image
using autocorrelation of the characteristic values of the first image and
the second image, and obtaining a corresponding area in both of the first
image and the second image; and comparing the characteristic values of
the corresponding area in both of the first image and the second image,
and obtaining an area of motion in the monitored scene, according to
differences in the characteristic values of the corresponding area in the
first image and the second image.

3. The method according to claim 1, wherein the person detection method
is a template matching method using neural network training and adaptive
boosting.

4. The method according to claim 1, further comprising: adjusting the
lens module of the image capture device according to movement data of the
area of motion to focus the lens module on the person in the area of
motion.

5. The method according to claim 1, further comprising: zooming in the
lens module of the image capture device.

6. An image capture device, comprising: a lens module; a storage device;
at least one processor; and one or more modules that are stored in the
storage device and are executed by the at least one processor, the one or
more modules comprising instructions: to obtain a plurality of images of
a monitored scene, the images being captured using the lens module of the
image capture device; to detect an area of motion in the monitored scene
from the obtained images; and to check for a person in the area of motion
using a person detection method.

7. The image capture device according to claim 6, wherein the instruction
to detect an area of motion in the monitored scene from the obtained
images comprises: obtaining a first image of the monitored scene at a
first time from the obtained images, and calculating characteristic
values of the first image; obtaining a second image of the monitored
scene at a second time continuous with the first time, and calculating
the characteristic values of the second image; comparing the first image
with the second image using autocorrelation of the characteristic values
of the first image and the second image, and obtaining a corresponding
area in both of the first image and the second image; and comparing the
characteristic values of the corresponding area in both of the first
image and the second image, and obtaining an area of motion in the
monitored scene, according to differences in the characteristic values of
the corresponding area in the first image and the second image.

8. The image capture device according to claim 6, wherein the person
detection method is a template matching method using neural network
training and adaptive boosting.

9. The image capture device according to claim 6, wherein the one or more
modules further comprise instructions: to adjust the lens module of the
image capture device according to movement data of the area of motion to
focus the lens module on the person in the area of motion.

10. The image capture device according to claim 6, wherein the one or
more modules further comprise instructions: to zoom in the lens module of
the image capture device.

11. A non-transitory storage medium having stored thereon instructions
that, when executed by a processor of an image capture device, causes the
processor to perform a method for detecting a person using the image
capture device, the image capture device being installed in an orbital
system, the method comprising: obtaining a plurality of images of a
monitored scene, the images being captured using a lens module of the
image capture device; detecting an area of motion in the monitored scene
from the obtained images; and checking for a person in the area of motion
using a person detection method.

12. The non-transitory storage medium according to claim 11, wherein the
step of detecting an area of motion in the monitored scene from the
obtained images comprises: obtaining a first image of the monitored scene
at a first time from the obtained images, and calculating characteristic
values of the first image; obtaining a second image of the monitored
scene at a second time continuous with the first time, and calculating
the characteristic values of the second image; comparing the first image
with the second image using autocorrelation of the characteristic values
of the first image and the second image, and obtaining a corresponding
area in both of the first image and the second image; and comparing the
characteristic values of the corresponding area in both of the first
image and the second image, and obtaining an area of motion in the
monitored scene, according to differences in the characteristic values of
the corresponding area in the first image and the second image.

13. The non-transitory storage medium according to claim 11, wherein the
person detection method is a template matching method using neural
network training and adaptive boosting.

14. The non-transitory storage medium according to claim 11, wherein the
method further comprises: adjusting the lens module of the image capture
device according to movement data of the area of motion to focus the lens
module on the person in the area of motion.

15. The non-transitory storage medium according to claim 11, wherein the
method further comprises: zooming in the lens module of the image capture
device.

16. The non-transitory storage medium according to claim 11, wherein the
medium is selected from the group consisting of a hard disk drive, a
compact disc, a digital video disc, and a tape drive.

Description:

BACKGROUND

[0001] 1. Technical Field

[0002] Embodiments of the present disclosure relate to security
surveillance technology, and particularly to an image capture device and
method for detecting a person using the image capture device.

[0003] 2. Description of Related Art

[0004] Image capture devices have been used to perform security
surveillance by capturing images of monitored scenes, and sending the
captured images to a monitor computer. The image capture device may
detect the presence of a person by examining an entire image captured by
the image capture devices using a person detection method. If the
captured image is large (e.g., a high definition image), a lot of time is
wasted checking all data of the image to detect the person. Therefore, an
efficient method for detecting a person using the image capture device is
desired.

BRIEF DESCRIPTION OF THE DRAWINGS

[0005] FIG. 1 is a block diagram of one embodiment of an image capture
device.

[0006] FIG. 2 is a block diagram of one embodiment of a person detection
system.

[0007] FIG. 3 is a flowchart of one embodiment of a method for detecting a
person using the image capture device.

[0008] FIG. 4 is a schematic diagram of one embodiment of a motion area.

[0009] FIG. 5 is a schematic diagram of one embodiment of detecting a
person in the motion area in FIG. 4.

DETAILED DESCRIPTION

[0010] All of the processes described below may be embodied in, and fully
automated via, functional code modules executed by one or more general
purpose electronic devices or processors. The code modules may be stored
in any type of non-transitory readable medium or other storage device.
Some or all of the methods may alternatively be embodied in specialized
hardware. Depending on the embodiment, the non-transitory readable medium
may be a hard disk drive, a compact disc, a digital video disc, a tape
drive or other suitable storage medium.

[0011] FIG. 1 is a block diagram of one embodiment of an image capture
device 2. In one embodiment, the image capture device 2 includes a person
detection system 20, a lens module 21, a storage device 22, a driving
unit 23, and at least one processor 24. The person detection system 20
may be used to detect an area of motion in a monitored scene from images
captured by the lens module 21, and further detect a person in the area
of motion. A detailed description will be given in the following
paragraphs.

[0012] In one embodiment, the image capture device 2 may be a speed dome
camera or pan/tilt/zoom (PTZ) camera, for example. The monitored scene
may be the interior of a warehouse or other important place.

[0013] The lens module 21 captures a plurality of images of the monitored
scene. In one embodiment, the lens module 21 may include a charge coupled
device (CCD) as well as lenses. The driving unit 23 may be used to aim,
focus, and zoom the lens module 21 of the image capture device 2. In one
embodiment, the driving unit 23 may be one or more driving motors.

[0014] In one embodiment, the person detection system 20 may include one
or more modules, for example, an image obtaining module 201, a motion
detection module 202, a person detection module 203, and a lens
adjustment module 204. The one or more modules 201-204 may comprise
computerized code in the form of one or more programs that are stored in
the storage device 22 (or memory). The computerized code includes
instructions that are executed by the at least one processor 24 to
provide functions for the one or more modules 201-204.

[0015] FIG. 3 is a flowchart of one embodiment of a method for detecting a
person using the image capture device 2. Depending on the embodiment,
additional blocks may be added, others removed, and the ordering of the
blocks may be changed.

[0016] In block S1, the image obtaining module 201 obtains a plurality of
images of a monitored scene captured using the lens module 21 of the
image capture device 2. In one embodiment, the lens module 21 captures an
images of the monitored scene after a preset time interval (e.g., five
seconds).

[0017] In block S2, the motion detection module 202 detects an area of
motion in the monitored scene from the obtained images. In one
embodiment, the area of motion is regarded as an area of the monitored
scene in which a moving object is detected. A detailed description is
provided as follows.

[0018] First, the motion detection module 202 obtains a first image of the
monitored scene at a first time from the obtained images, and calculates
characteristic values (e.g., gray values of blue color) of the first
image. Second, the motion detection module 202 obtains a second image of
the monitored scene at a second time continuous with the first time, and
calculates the characteristic values of the second image. Third, the
motion area detection module 202 compares the first image with the second
image using autocorrelation of the characteristic values of the first
image and the second image, and obtains a corresponding area in both of
the first image and the second image. Fourth, the motion detection module
202 compares the characteristic values of the corresponding area in both
of the first image and the second image, and obtains an area of motion in
the monitored scene if motion has occurred, according to differences in
the characteristic values of the corresponding area in the first image
and the second image.

[0019] In block S3, the motion detection module 202 determines if motion
has occurred in the monitored scene. If motion is detected in the
monitored scene, the procedure goes to block S4. If motion is not
detected in the monitored scene, the procedure returns to block S2.

[0020] In block S4, the person detection module 203 checks for a person in
the area of motion using a person detection method. In one embodiment,
the person detection method may be a template matching method using
neural network training algorithm and adaptive boosting (AdaBoost)
algorithm. Referring to FIG. 4 and FIG. 5, an area of motion 41 is
detected in a captured image 40 by the motion detection module 202 in
FIG. 4, and a person 42 is further detected in the area of motion 41 by
the person detection module 203 in FIG. 5.

[0021] In other embodiments, the lens adjustment module 204 adjusts the
lens module 21 of the image capture device 2 according to movement data
of the area of motion using the driving unit 23 to focus and zoom in on
the lens module 21 on the person in the area of motion. In one
embodiment, the movement data of the area of motion may include, but is
not limited to, a direction of movement and a distance of movement. For
example, the lens adjustment module 204 determines that the lens module
21 should move towards the left if the direction of movement in the area
of motion is to the left, or determines that the lens module 21 should be
moved towards the right if the direction of movement in the area of
motion is to the right.

[0022] It should be emphasized that the above-described embodiments of the
present disclosure, particularly, any embodiments, are merely possible
examples of implementations, merely set forth for a clear understanding
of the principles of the disclosure. Many variations and modifications
may be made to the above-described embodiment(s) of the disclosure
without departing substantially from the spirit and principles of the
disclosure. All such modifications and variations are intended to be
included herein within the scope of this disclosure and the present
disclosure and protected by the following claims.